4,047 research outputs found
Understanding Learned Models by Identifying Important Features at the Right Resolution
In many application domains, it is important to characterize how complex
learned models make their decisions across the distribution of instances. One
way to do this is to identify the features and interactions among them that
contribute to a model's predictive accuracy. We present a model-agnostic
approach to this task that makes the following specific contributions. Our
approach (i) tests feature groups, in addition to base features, and tries to
determine the level of resolution at which important features can be
determined, (ii) uses hypothesis testing to rigorously assess the effect of
each feature on the model's loss, (iii) employs a hierarchical approach to
control the false discovery rate when testing feature groups and individual
base features for importance, and (iv) uses hypothesis testing to identify
important interactions among features and feature groups. We evaluate our
approach by analyzing random forest and LSTM neural network models learned in
two challenging biomedical applications.Comment: First two authors contributed equally to this work, Accepted for
presentation at the Thirty-Third AAAI Conference on Artificial Intelligence
(AAAI-19
A Study of Deep Learning Robustness Against Computation Failures
For many types of integrated circuits, accepting larger failure rates in
computations can be used to improve energy efficiency. We study the performance
of faulty implementations of certain deep neural networks based on pessimistic
and optimistic models of the effect of hardware faults. After identifying the
impact of hyperparameters such as the number of layers on robustness, we study
the ability of the network to compensate for computational failures through an
increase of the network size. We show that some networks can achieve equivalent
performance under faulty implementations, and quantify the required increase in
computational complexity
A Deep Network with Visual Text Composition Behavior
While natural languages are compositional, how state-of-the-art neural models
achieve compositionality is still unclear. We propose a deep network, which not
only achieves competitive accuracy for text classification, but also exhibits
compositional behavior. That is, while creating hierarchical representations of
a piece of text, such as a sentence, the lower layers of the network distribute
their layer-specific attention weights to individual words. In contrast, the
higher layers compose meaningful phrases and clauses, whose lengths increase as
the networks get deeper until fully composing the sentence.Comment: accepted to ACL201
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